Towards learning healthcare systems in Italy: opportunities and challenges of AI at point-of-care.

Abstract

In Italy, the growing enthusiasm for artificial intelligence (AI) in healthcare contrasts with significant infrastructural, cultural, and trust-related barriers hindering its real-world adoption. Moving beyond the hype requires a systems thinking approach, proposing the learning health system (LHS) framework as a structured path for integration. We highlight the complementary roles of AI models: traditional machine learning (ML) is proven for diagnostics and prognostics, while large language models (LLMs) excel at administrative tasks and can structure unstructured data to train robust ML tools. The LHS cycle reveals key challenges for Italy: moving from Practice-to-Data requires overcoming data fragmentation; from Data-to-Knowledge involves transforming data into insights while mitigating bias; and from Knowledge-to-Practice necessitates bridging the gap between evidence and clinical workflow by building trust and AI literacy. Ultimately, successful and equitable AI implementation depends on a holistic strategy combining infrastructure development, multidisciplinary collaboration, and robust governance to enhance the quality and sustainability of the national healthcare system.